Abstract
Freezing of Gait (FoG) in Parkinson Disease (PD) is a sudden episode characterized by a brief failure to walk. The aim of this study is to detect FoG episodes using a multi-sensor device for data acquisition, and Gaussian neural networks as a classification tool. Thus we have built a multi sensor prototype that detects FoG using new indicators like the variation of the inter-foot distance or the knee angle. Data are acquired from PD patients having FoG as a major symptom. The major social challenge is obtaining the acknowledgment of patients to participate in our study, whereas the main technical difficulty is extracting efficient features from various walking behaviors. For that purpose, the acquired signals are analyzed in order to extract both time and frequency domain features that separate the FoG class from the other gaits modes. Due to the complexity of FoG episodes, the optimal features are then extracted using Principal Component Analysis technique. Another contribution is to introduce the combined data into the Gaussian Neural Network (GNN) classification method, that is a new technique used for FoG detection, and has been developed in our previous works. Moreover, the classical thresholding method is implemented to compare and validate the GNN method. Results showed the feasibility of integrating the chosen sensors, in addition to the effectiveness of combining data from different types of sensors on the classification rate. The efficiency rate of classification in the proposed method is about 87 %.